
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb(Author)
Wiley (Publisher)
Published on 22. May 2001
Book
Hardback
XX, 498 pages
978-0-471-87339-6 (ISBN)
Description
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.
* Comprehensive coverage of this growing area of research
* Carefully introduces each algorithm with examples and in-depth discussion
* Includes many applications to real-world problems, including engineering design and scheduling
* Includes discussion of advanced topics and future research
* Can be used as a course text or for self-study
* Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms
The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Reviews / Votes
"...discusses two multi-objective optimization procedures, namely the ideal procedure and the preference-based one." (Zentralblatt MATH, Vol. 970, 2001/20)More details
Series
Edition
1. Auflage
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 250 mm
Width: 175 mm
Thickness: 33 mm
Weight
1096 gr
ISBN-13
978-0-471-87339-6 (9780471873396)
Schweitzer Classification
Other editions
Additional editions

Book
10/2008
Wiley
€95.50
Shipment within 10-20 days
Person
Kalyanmoy Deb is an Indian computer scientist. Since 2013, Deb has held the Herman E. & Ruth J. Koenig Endowed Chair in the Department of Electrical and Computing Engineering at Michigan State University, which was established in 2001.
Content
Foreword.
Preface.
Prologue.
Multi-Objective Optimization.
Classical Methods.
Evolutionary Algorithms.
Non-Elitist Multi-Objective Evolutionary Algorithms.
Elitist Multi-Objective Evolutionary Algorithms.
Constrained Multi-Objective Evolutionary Algorithms.
Salient Issues of Multi-Objective Evolutionary Algorithms.
Applications of Multi-Objective Evolutionary Algorithms.
Epilogue.
References.
Index.